Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R

In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling,off-the-shelf software for model fitting does not as of yet exist. convolution-based models are highly flexible yet notoriously difficult to fit,even with relatively small data sets. the general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods,and as a result most new models are simply compared to stationary models. using a convolution-based approach,we present a new nonstationary covariance function for spatial gaussian process models that allows for efficient computing in two ways: first,by representing the spatially-varying parameters via a discrete mixture or “mixture component” model,and second,by estimating the mixture component parameters through a local likelihood approach. in order to make computations for a convolutionbased nonstationary spatial model readily available,this paper also presents and describes the convospat package for r. the nonstationary model is fit to both a synthetic data set and a real data application involving annual precipitation to demonstrate the capabilities of the package. © 2017,american statistical association. all rights reserved.